An investigation into the dependence of virtual observation point-based specific absorption rate calculation complexity on number of channels
Stephan Orzada, Safi Akash, Thomas M. Fiedler, Fabian J. Kratzer, Mark E. Ladd
Abstract
Purpose
This study aims to find a relation between the number of channels and the computational burden for specific absorption rate (SAR) calculation using virtual observation point-based SAR compression.
Methods
Eleven different arrays of rectangular loops covering a cylinder of fixed size around the head of an anatomically correct voxel model were simulated. The resulting Q-matrices were compressed with 2 different compression algorithms, with the overestimation fixed to a certain fraction of worst-case SAR, median SAR, or minimum SAR. The latter 2 were calculated from 1e6 normalized random excitation vectors.
Results
The number of virtual observation points increased with the number of channels to the power of 2.3–3.7, depending on the compression algorithm when holding the relative error fixed. Together with the increase in the size of the Q-matrices (and therefore the size of the virtual observation points), the total increase in computational burden with the number of channels was to the power of 4.3–5.7.
Conclusion
The computational cost emphasizes the need to use the best possible compression algorithms when moving to high channel counts.